Social media is becoming increasingly prevalent with the advent of new technologies, advances in communication channels, and other factors. Social media platforms and websites, such as FACEBOOK® social networking service, TWITTER® social networking service, and others, attract users to post and share messages for a variety of purposes, including daily conversation, sharing uniform resource locators (URLs) and other data, among other uses. Companies, individuals, and other entities may desire to detect topics described in social media data, for the purpose of discovering “trending topics,” tracking online users interests, understanding users' complaints or mentions about a product or service, or other purposes. For example when a company launches a marketing campaign for a newly-released product, the company may desire to investigate whether the product or service is relevant to the trending topics recently discussed on social media, or whether the product or service might be desired by selected users.
Existing topic detection methodologies are generally based on probabilistic language models, such as Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). However, these analyses assume that a single document contains rich information, which is not applicable to some forms of social media. For example, a “tweet” from the TWITTER® social networking service is limited to 140 characters. In addition, the detected topics by probabilistic methods are difficult to interpret in a human-understandable way, in part because the methods can only identify a set of “key” terms associated with a set of numerical values to indicate how important the terms are for each detected topic. An additional concern of topic detection in social media is the issue of scalability. More particularly, a large volume of tweets and other data postings are posted on various social networking websites and platforms every day in an order of hundreds of millions.
Therefore, it may be desirable to have systems and methods for detecting topics in social media. In particular, it may be desirable to have systems and methods for leveraging available data to construct topic models and interpret generated results in a human-understandable way.